Improving system performance for stochastic activity network: A simulation approach

  • Authors:
  • Won Kyung Kim;K. Paul Yoon;Yongbeom Kim;Gary J. Bronson

  • Affiliations:
  • Department of Computer Engineering, Kyungnam University, Wolyungdong 449, Masan Kyungnam 631-701, Republic of Korea;Information Systems and Decision Sciences Department, Fairleigh Dickinson University, 1000 River Rd., Teaneck, NJ 07666, USA;Information Systems and Decision Sciences Department, Fairleigh Dickinson University, 1000 River Rd., Teaneck, NJ 07666, USA;Information Systems and Decision Sciences Department, Fairleigh Dickinson University, 1000 River Rd., Teaneck, NJ 07666, USA

  • Venue:
  • Computers and Industrial Engineering
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

An activity network with returning loop activities has a wide variety of applications, but can cause a heavy computational burden for large networks. Moreover, if an activity processing time and/or the probability of taking a particular activity changes when the number of activity visits is added, the computation is very complicated and difficult. We propose a simulation approach to deal with stochastic activity networks consisting of multiple terminal nodes, no limit on looping activities, non-constant activity selection probabilities, and non-deterministic activity times following arbitrary distributions. Probability and time control functions are introduced to reflect the acceleration, or learning effect, of repeated activities. Performance measures such as system success/failure probabilities, time to completion/success/failure times, time between success/failure, and the pth percentile times of a project are obtained. A series of sensitivity analysis was performed to understand the trend and behavior of system performance. A cost function is developed to find an optimal strategy by manipulating control factors. To illustrate the efficacy of this simulation approach a new drug discovery and development project was analyzed. The Promodel simulation language was used for performance evaluations, and the SimRunner optimization tool for obtaining the optimal solution.